Service restrictions from February 12-22, 2026—more information on the University Library website

Result: Automatic classification of Giardia infection from stool microscopic images using deep neural networks.

Title:
Automatic classification of Giardia infection from stool microscopic images using deep neural networks.
Authors:
Yarahmadi P; Department of Biomedical Engineering, Faculty of Advanced Medical Sciences, Tabriz University of Medical Sciences Tabriz, Iran., Ahmadpour E; Department of Parasitology and Mycology, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran., Moradi P; Department of Computer Engineering, University of Kurdistan, Sanandaj, Iran., Samadzadehaghdam N; Department of Biomedical Engineering, Faculty of Advanced Medical Sciences, Tabriz University of Medical Sciences Tabriz, Iran.
Source:
BioImpacts : BI [Bioimpacts] 2024 Sep 24; Vol. 15, pp. 30272. Date of Electronic Publication: 2024 Sep 24 (Print Publication: 2025).
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Tabriz University of Medical Sciences Country of Publication: Iran NLM ID: 101558125 Publication Model: eCollection Cited Medium: Print ISSN: 2228-5652 (Print) Linking ISSN: 22285652 NLM ISO Abbreviation: Bioimpacts Subsets: PubMed not MEDLINE
Imprint Name(s):
Original Publication: Tabriz : Tabriz University of Medical Sciences
References:
BMC Infect Dis. 2020 Feb 24;20(1):174. (PMID: 32093615)
PeerJ. 2018 Apr 16;6:e4568. (PMID: 29682411)
Nature. 2015 May 28;521(7553):436-44. (PMID: 26017442)
Cytometry A. 2021 Nov;99(11):1123-1133. (PMID: 33550703)
J Clin Microbiol. 2020 May 26;58(6):. (PMID: 32295888)
IEEE Trans Image Process. 2018 Oct 31;:. (PMID: 30387733)
RSC Adv. 2021 May 13;11(29):17603-17610. (PMID: 35480202)
Int J Parasitol. 2000 Nov;30(12-13):1259-67. (PMID: 11113253)
IEEE Trans Image Process. 2020 Jan 09;:. (PMID: 31940532)
Nat Mater. 2019 May;18(5):435-441. (PMID: 31000803)
Int J Parasitol. 2013 Nov;43(12-13):943-56. (PMID: 23856595)
Radiol Phys Technol. 2017 Sep;10(3):257-273. (PMID: 28689314)
Trends Parasitol. 2006 May;22(5):203-8. (PMID: 16545611)
Appl Environ Microbiol. 2005 Jan;71(1):80-4. (PMID: 15640173)
Contributed Indexing:
Keywords: Classification; Deep neural network; Giardiasis; Machine learning
Entry Date(s):
Date Created: 20250331 Latest Revision: 20250402
Update Code:
20250407
PubMed Central ID:
PMC11954735
DOI:
10.34172/bi.30272
PMID:
40161947
Database:
MEDLINE

Further Information

Introduction: Giardiasis is a common intestinal infection caused by the Giardia lamblia parasite, and its rapid and accurate diagnosis is crucial for effective treatment. The automatic classification of Giardia infection from stool microscopic images plays a vital role in this diagnosis process. In this study, we applied deep learning-based models to automatically classify stool microscopic images into three categories, namely, normal, cyst, and trophozoite.
Methods: Unlike previous studies focused on images acquired from drinking water samples, we specifically targeted stool samples. In this regard, we collected a dataset of 1610 microscopic digital images captured by a smartphone with a resolution of 2340 × 1080 pixels from stool samples under the Nikon YS100 microscope. First, we applied CLAHE (Contrast Limited Adaptive Histogram Equalization) histogram equalization a method to enhance the image quality and contrast. We employed three deep learning models, namely Xception, ResNet-50, and EfficientNet-B0, to evaluate their classification performance. Each model was trained on the dataset of microscopic images and fine-tuned using transfer learning techniques.
Results: Among the three deep learning models, EfficientNet-B0 demonstrated superior performance in classifying Giardia lamblia parasites from stool microscopic images. The model achieved precision, accuracy, recall, specificity, and F1-score values of 0.9599, 0.9629, 0.9619, 0.9821, and 0.9607, respectively.
Conclusion: The EfficientNet-B0 showed promising results in accurately identifying normal, cyst, and trophozoite forms of Giardia lamblia parasites. This automated classification approach can provide valuable assistance to laboratory science experts and parasitologists in the rapid and accurate diagnosis of giardiasis, ultimately improving patient care and treatment outcomes.
(© 2025 The Author(s).)

The authors declare no conflict of interest.